Method and system for reconstructing super-resolution image

ABSTRACT

A method for reconstructing a super-resolution image, including: 1) reducing the resolution of an original high-resolution image to obtain an equal low-resolution image, respectively expressed as matrix forms yh and yl; 2) respectively conducting dictionary training on yl and yhl to obtain a low-resolution image dictionary Dl; 3) dividing the sparse representation coefficients αl and αhl into training sample coefficients αl_train and αhl_train and test sample coefficients αl_test and αhl_test; 4) constructing an L-layer deep learning network using a root-mean-square error as a cost function; 5) iteratively optimizing network parameters so as to minimize the cost function by using the low-resolution image sparse coefficient αl_train as the input of the deep learning network; 6) inputting the low-resolution image sparse coefficient αl_test as the test portion into the trained deep learning network in 5), outputting to obtain a predicted difference image sparse coefficient {circumflex over (α)}hl_test, computing an error between the {circumflex over (α)}hl_test.

CROSS-REFERENCE TO RELATED APPLICATIONS

Pursuant to 35 U.S.C. § 119 and the Paris Convention Treaty, this application claims the benefit of Chinese Patent Application No. 201610216592.X filed Apr. 8, 2016, the contents of which are incorporated herein by reference. Inquiries from the public to applicants or assignees concerning this document or the related applications should be directed to: Matthias Scholl P. C., Attn.: Dr. Matthias Scholl Esq., 245 First Street, 18th Floor, Cambridge, Mass. 02142.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure belongs to the technical field of remote sensing image processing, and relates to a method and system for reconstructing a super-resolution image in combination with sparse representation and deep learning.

Description of the Related Art

Image super-resolution refers to reconstructing high-resolution images that include more details from a series of lower-resolution images, and has an important application value in such fields as target recognition and positioning of a remote sensing image, environmental monitoring, medical imaging, and the like. Image super-resolution breaks through the limitation of the resolution of a sensor itself, obtains images with higher quality and higher resolution on the basis of the existing image acquisition technology, and provides a foundation for further image analysis.

A traditional image super-resolution needs many low-resolution images of the same scenario as information sources to reconstruct the high-resolution images by integrating the low-resolution images based on reasonable assumption of the mapping from the high-resolution images to the low-resolution images as well as prior information. A common prior model includes a Gaussian prior model, a Markov random field, and the like. However, from the viewpoint of mathematics, since the number of the low-resolution images is inadequate, the above super-resolution reconstruction problem is actually an ill-posed inverse problem and a reconstruction result may not be unique.

Learning-based super-resolution refers to obtaining a certain correspondence relationship between a high-resolution image block and a low-resolution image block by a machine learning method. Conventionally, sparse decomposition coefficients of the low-resolution images are all directly used for the integration of the high-resolution images, or a linear relationship is forced to exist between high-resolution and low-resolution sparse representation coefficients in a process for solving. In fact, the sparse representation coefficients of the same image in different space dimensions are different. Therefore, such simple processing mode is often an important factor that restricts an image super-resolution effect.

SUMMARY OF THE INVENTION

Aiming at the defects of the existing image super-resolution reconstruction technology, the purpose of the present disclosure is to provide a new technical solution for using a deep learning network to learn a mapping relationship between sparse representation coefficients of a high-resolution image and a low-resolution image.

The technical solution of the present disclosure is as follows: A method for reconstructing a super-resolution image in combination with sparse representation and deep learning, comprising the following:

a) reducing the resolution of an original high-resolution image to obtain an equal low-resolution image, respectively expressed as matrix forms y_(h) and y_(l), and computing a difference portion between two images, y_(hl)=y_(h)−y_(l);

-   -   b) respectively conducting dictionary training on y_(l) and         y_(hl) to obtain a low-resolution image dictionary D_(l), a         difference image dictionary D_(hl) and corresponding sparse         representation coefficients α_(l) and α_(hl);     -   c) dividing the sparse representation coefficients α_(l) and         α_(hl) into training sample coefficients α_(l) _(_) _(train) and         α_(hl) _(_) _(train) and test sample coefficients α_(l) _(_)         _(test) and α_(hl) _(_) _(test);     -   d) constructing an L-layer deep learning network using a         root-mean-square error as a cost function;     -   e) iteratively optimizing network parameters so as to minimize         the cost function by using the low-resolution image sparse         coefficient α_(l) _(_) _(train) as the input of the deep         learning network, using the corresponding difference image         sparse coefficient α_(hl) _(_) _(train) as a target output and         using {circumflex over (α)}_(hl) _(_) _(train) as a         network-predicted difference image sparse coefficient, until a         trained deep learning network is obtained;     -   f) inputting the low-resolution image sparse coefficient α_(l)         _(_) _(test) as the test portion into the trained deep learning         network in e, outputting to obtain a predicted difference image         sparse coefficient {circumflex over (α)}_(hl) _(_) _(test),         computing an error between the {circumflex over (α)}_(hl) _(_)         _(test) and a corresponding true difference image sparse         coefficient α_(hl) _(_) _(test), and verifying that the deep         learning network obtained by training in e is a mapping between         the low-resolution image sparse coefficient and the difference         image sparse coefficient when the error is less than a given         threshold;     -   g) expressing the low-resolution image to be subjected to         resolution increase as the matrix form z_(l), expressing z_(l)         with the dictionary D_(l), recording a corresponding sparse         coefficient as β_(l), inputting β_(l) into the trained deep         learning network to obtain a predicted difference image sparse         coefficient β_(hl), reconstructing a difference portion         {circumflex over (z)}_(hl) with the dictionary D_(hl); and         finally reducing {circumflex over (z)}_(h)={circumflex over         (z)}_(hl)+z_(l) into an image form to reconstruct a         corresponding high-resolution image z_(h).

Moreover, in a), firstly, a high-resolution image in a training sample library is cut into N d×dimage blocks; the resolution of each image block is reduced to obtain N corresponding low-resolution image blocks; then column vectors formed by stretching the high-resolution image blocks compose a matrix y_(h)∈R^(d) ² ^(×N), and column vectors formed by stretching the low-resolution image blocks compose a matrix y_(l)∈R^(d) ² ^(×N); and the difference portion y_(hl)=y_(h)−y_(l) of two matrixes is obtained through computation.

Moreover, in b), dictionary training is respectively conducted on y_(l) and y_(hl) to obtain a corresponding low-resolution image dictionary D_(l), a difference image dictionary D_(hl) and corresponding sparse representation coefficients α_(l) and α_(hl), equivalent to solving optimization problems as follows:

$\begin{matrix} {\min\limits_{D_{l},\alpha_{l}}\left. ||\alpha_{l}||{}_{0}\mspace{14mu}{{subject}\mspace{14mu}{to}}\mspace{14mu}||{y_{l} - {D_{l}\alpha_{l}}}\mathop{\text{||}}_{F}^{2}{\leq ɛ} \right.} & (1) \\ {\min\limits_{D_{hl},\alpha_{hl}}\left. ||\alpha_{hl}||{}_{0}\mspace{14mu}{{subject}\mspace{14mu}{to}}\mspace{14mu}||{y_{hl} - {D_{hl}\alpha_{hl}}}\mathop{\text{||}}_{F}^{2}{\leq ɛ} \right.} & (2) \end{matrix}$ wherein ε is a reconstruction error threshold.

Moreover, in d), the constructed deep learning network comprises L layers; the output of each layer is recorded as x^(l), l=0, 1, 2, . . . , L, wherein x⁰ is a network input and then the output of an l_(th) layer is: x ^(l) =f _(l)(W ^(l) x ^(l-1) +b ^(l)), l=1,2,. . . ,L  (3) wherein W^(l) and b^(l) respectively indicate the weight and the bias term of the l_(th) layer, f_(l)(⋅) is an activation function of the l_(th) layer, and the output of the l_(th) layer is a network prediction.

Moreover, in e), an implicit relationship between the low-resolution image sparse coefficient α_(l) _(_) _(train) and the difference image sparse coefficient α_(hl) _(_) _(train) is trained by the deep learning network, and by using the low-resolution image sparse coefficient α_(l) _(_) _(train) as the input of the deep learning network and using the difference image sparse coefficient α_(hl) _(_) _(train) as a supervision, the network-predicted difference image sparse coefficient is recorded as {circumflex over (α)}_(hl) _(_) _(train) =f _(L)( . . . f _(l)(W ^(l)α_(l) _(_) _(train) +b ¹))  (4) a root-mean-square error with a cost function of α_(hl) _(_) _(train)−{circumflex over (α)}_(hl) _(_) _(train) is taken

$\begin{matrix} {{MSRE} = \frac{\left. ||{\alpha_{{hl}_{—}{train}} - {\hat{\alpha}}_{{hl}_{—}{train}}}||_{F}^{2} \right.}{mn}} & (5) \end{matrix}$ wherein m and n are respectively the number of dictionary elements and the number of training samples; and the network parameters are optimized iteratively so as to minimize a loss function MSRE, thereby completing network training.

The present disclosure further provides a system for reconstructing a super-resolution image in combination with sparse representation and deep learning, the system comprising:

-   -   a first module used for reducing the resolution of an original         high-resolution image to obtain an equal low-resolution image,         respectively expressed as matrix forms y_(h) and y_(l), and         computing a difference portion between two images,         y_(hl)=y_(h)−y_(l);     -   a second module used for respectively conducting dictionary         training on y_(l) and y_(hl) to obtain a low-resolution image         dictionary D₁, a difference image dictionary D_(hl) and         corresponding sparse representation coefficients α_(l) and         α_(hl);     -   a third module used for dividing the sparse representation         coefficients α_(l) and α_(hl) into training sample coefficients         α_(l) _(_) _(train) and α_(hl) _(_) _(train) and test sample         coefficients α_(l) _(_) _(test) and α_(hl) _(_) _(test);     -   a fourth module used for constructing an L-layer deep learning         network using a root-mean-square error as a cost function;     -   a fifth module used for iteratively optimizing network         parameters so as to minimize the cost function by using the         low-resolution image sparse coefficient α_(l) _(_) _(train) as         the input of the deep learning network, using the corresponding         difference image sparse coefficient α_(hl) _(_) _(train) as a         target output and using {circumflex over (α)}_(hl) _(_) _(train)         as a network predicted difference image sparse coefficient,         until a trained deep learning network is obtained;     -   a sixth module used for inputting the low-resolution image         sparse coefficient α_(l) _(_) _(test) as the test portion into         the trained deep learning network in the fifth module,         outputting to obtain a predicted difference image sparse         coefficient {circumflex over (α)}_(hl) _(_) _(test), computing         an error between the {circumflex over (α)}_(hl) _(_) _(test) and         a corresponding true difference image sparse coefficient α_(hl)         _(_) _(test), and verifying that the deep learning network         obtained by training in the fifth module is a mapping between         the low-resolution image sparse coefficient and the difference         image sparse coefficient when the error is less than a given         threshold; and     -   a seventh module used for expressing the low-resolution image to         be subjected to resolution increase as the matrix form z_(l),         expressing z_(l) with the dictionary D_(l), recording a         corresponding sparse coefficient as β_(l), inputting β_(l) into         the trained deep learning network to obtain a predicted         difference image sparse coefficient β_(hl), reconstructing a         difference portion {circumflex over (z)}_(hl), with the         dictionary D_(hl); and finally reducing {circumflex over         (z)}={circumflex over (z)}_(hl)+z_(l) into an image form to         reconstruct a corresponding high-resolution image z_(h).

Moreover, in the first module, firstly, a high-resolution image in a training sample library is cut into N d×d image blocks; the resolution of each image block is reduced to obtain N corresponding low-resolution image blocks; then column vectors formed by stretching the high-resolution image blocks compose a matrix y_(h)∈R^(d) ² ^(×N), and column vectors formed by stretching the low-resolution image blocks compose a matrix y_(l)∈R^(d) ² ^(×N), and the difference portion y_(hl)=y_(h)−y_(l) of two matrixes is obtained through computation.

Moreover, in the second module, dictionary training is respectively conducted on y_(l) and y_(hl) to obtain a corresponding low-resolution image dictionary D_(l), a difference image dictionary D_(hl) and corresponding sparse representation coefficients α_(l) and α_(hl), equivalent to solving optimization problems as follows:

$\begin{matrix} {\min\limits_{D_{l},\alpha_{l}}\left. ||\alpha_{l}||{}_{0}\mspace{14mu}{{subject}\mspace{14mu}{to}}\mspace{14mu}||{y_{l} - {D_{l}\alpha_{l}}}\mathop{\text{||}}_{F}^{2}{\leq ɛ} \right.} & (6) \\ {\min\limits_{D_{hl},\alpha_{hl}}\left. ||\alpha_{hl}||{}_{0}\mspace{14mu}{{subject}\mspace{14mu}{to}}\mspace{14mu}||{y_{hl} - {D_{hl}\alpha_{hl}}}\mathop{\text{||}}_{F}^{2}{\leq ɛ} \right.} & (7) \end{matrix}$ wherein ε is a reconstruction error threshold.

Moreover, in the fourth module, the constructed deep learning network comprises L layers; the output of each layer is recorded as x^(l), l=0, 1, 2, . . . , L, wherein x⁰ is a network input and then the output of an l_(th) layer is: x ^(l) =f _(l)(W ^(l) x ^(l-1) +b ^(l)), l=1,2, . . . ,L  (8) wherein W^(l) and b^(l) respectively indicate the weight and the bias term of the l_(th) layer, f_(l)(⋅) is an activation function of the l_(th) layer, and the output of the l_(th) layer is a network prediction.

Moreover, in the fifth module, an implicit relationship between the low-resolution image sparse coefficient α_(l) _(_) _(train) and the difference image sparse coefficient α_(hl) _(_) _(train) is trained by the deep learning network, and by using the low-resolution image sparse coefficient α_(l) _(_) _(train) as the input of the deep learning network and using the difference image sparse coefficient α_(hl) _(_) _(train) as a supervision, the network-predicted difference image sparse coefficient is recorded as {circumflex over (α)}_(hl) _(_) _(train) =f _(L)( . . . f ₁(W ¹α_(l) _(_) _(train) +b ¹))  (9) a root-mean-square error with a cost function of α_(hl) _(_) _(train)−{circumflex over (α)}_(hl) _(_) _(train) is taken

$\begin{matrix} {{MSRE} = \frac{\left. ||{\alpha_{{hl}_{—}{train}} - {\hat{\alpha}}_{{hl}_{—}{train}}}||_{F}^{2} \right.}{mn}} & (10) \end{matrix}$ wherein m and n are respectively the number of dictionary elements and the number of training samples; and the network parameters are optimized iteratively so as to minimize a loss function MSRE, thereby completing network training.

The present disclosure overcomes the defects of the existing method which uses a dictionary-combined training mode to allow the high-resolution image and the low-resolution image to share the sparse representation coefficients, and adopts deep learning to fully learn a mapping relationship between the sparse representation coefficients of the low-resolution image and the difference image, so as to obtain a high-resolution reconstruction result with higher precision.

Compared with the existing method, the present disclosure has the advantages and the positive effects: in the image super-resolution reconstruction based on sparse representation, the existing method generally uses a dictionary-combined training mode to allow the high-resolution image and the low-resolution image to share the sparse representation coefficients, or simply performs a linear combination on the high-resolution image and the low-resolution image in a dictionary training process for performing training. However, the sparse representation coefficients of the same image in different space dimensions are often not in a simple linear mapping relationship. The present disclosure has the advantage that an implicit relationship between the sparse representation coefficients of the high-resolution image and the low-resolution image is learned from a great number of samples by the deep learning network, so that the image super-resolution reconstruction precision is higher.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of a method for reconstructing a super-resolution image of embodiments of the present disclosure.

FIG. 2 shows a trained dictionary of difference images. To acquire this dictionary, large amounts of difference images, which are the differences between the corresponding high and low resolution images, are acquired first and used for dictionary training with K-SVD algorithm.

FIG. 3 shows a trained dictionary of low-resolution images. Similar as the training process of difference image dictionary, enormous low resolution images are prepared first and used for dictionary training with K-SVD algorithm. The difference is that, to efficiently use the high frequency information, the first and second order derivatives of low images are both calculated. Then, they are combined together for low resolution image dictionary training.

FIG. 4 is an original remote sensing image.

FIG. 5 is a reconstructed high frequency information from image FIG. 4 with the present invention method. Firstly, the original image is sparsely decomposed with trained low resolution image dictionary. Secondly, the corresponding sparse coefficients are input to the trained deep learning network to be mapped to the sparse coefficients of difference image. Then, the difference image can be reconstructed with the corresponding dictionary and the mapped coefficients.

FIG. 6 is the reconstructed high resolution image of FIG. 4 with the present invention method, it is the sum of low resolution image and the reconstructed difference image.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The present disclosure will be further described below in combination with the drawings and the embodiments.

The embodiment of the present disclosure relates to the super-resolution reconstruction of a remote sensing image. As shown in FIGS. 1-6, the embodiment of the present disclosure comprises the following concrete steps:

a: Generating Data

In the present disclosure, firstly, a high-resolution image in a training sample library is cut into N d×d image blocks; the resolution of each image block is reduced to obtain N corresponding low-resolution image blocks; then column vectors formed by stretching the high-resolution image blocks compose a matrix y_(l)∈R^(d) ² ^(×N) (indicating that, y^(h) is a real matrix of d²×N), and a corresponding matrix y_(l)∈R^(d) ² ^(×N) of the low-resolution image is obtained in the same manner; and the difference portion y_(hl)=y_(h)−y_(l) of two matrixes is obtained through computation.

b: Dictionary Training and Corresponding Sparse Representation Coefficients

Dictionary training is respectively conducted on y_(l) and y_(hl) to obtain a corresponding low-resolution image dictionary D_(l), a difference image dictionary D_(hl) and corresponding sparse representation coefficients α_(l) and α_(hl), equivalent to solving optimization problems as follows:

$\begin{matrix} {\min\limits_{D_{l},\alpha_{l}}\left. ||\alpha_{l}||{}_{0}\mspace{14mu}{{subject}\mspace{14mu}{to}}\mspace{14mu}||{y_{l} - {D_{l}\alpha_{l}}}\mathop{\text{||}}_{F}^{2}{\leq ɛ} \right.} & (11) \\ {\min\limits_{D_{hl},\alpha_{hl}}\left. ||\alpha_{hl}||{}_{0}\mspace{14mu}{{subject}\mspace{14mu}{to}}\mspace{14mu}||{y_{hl} - {D_{hl}\alpha_{hl}}}\mathop{\text{||}}_{F}^{2}{\leq ɛ} \right.} & (12) \end{matrix}$ wherein ∥⋅∥_(F) is F-norm; ε is a reconstruction error threshold, generally a smaller value, and can be set according to concrete requirements in concrete implementation.

However, since the above l₀ norm constraint problem is an NP-hard problem, a greedy algorithm and the like is required for solving the problem. Tao, Candes, Donoho, et al. have proved that l₀ norm can be converted into l₁ norm for solving the problem when a signal satisfies a certain sparse condition. In this way, the above optimization problems can be converted into:

$\begin{matrix} {\min\limits_{D_{l},\alpha_{l}}\left. ||{y_{l} - {D_{l}\alpha_{l}}}\mathop{\text{||}}_{F}^{2}{+ \lambda}||\alpha_{l} \right.||_{1}} & (13) \\ {\min\limits_{D_{hl},\alpha_{hl}}\left. ||{y_{hl} - {D_{hl}\alpha_{hl}}}\mathop{\text{||}}_{F}^{2}{+ \lambda}||\alpha_{hl} \right.||_{1}} & (14) \end{matrix}$ wherein λ is a sparse constraint weight (a reference value λ is 0.15, and can be adjusted by those skilled in the art according to a need of a sparse term in concrete implementation). The above optimization problem is a convex optimization problem and can be solved by an optimization algorithm.

c: Dividing the Sparse Representation Coefficients into Training Sample Coefficients and Test Sample Coefficients

A dividing proportion can be preset by those skilled in the art in concrete implementation. In the embodiment, the ratio of the training samples to the test samples is about 2:1. Namely, ⅔ of the total samples are used as the training samples, and α_(l) _(_) _(train) and α_(hl) _(_) _(train) are respectively used for indicating the low-resolution image sparse coefficient and the difference image sparse coefficient for training; and the remaining ⅓ of the total samples are used as the test samples, and α_(l) _(_) _(test) and α_(hl) _(_) _(test) are respectively used for indicating the low-resolution image sparse coefficient and the difference image sparse coefficient for testing.

d: Constructing a Deep Learning Network

The deep learning network constructed in the embodiment is assumed to include L layers (L is recommended as 3, i.e., the network includes two hidden layers and one output layer and the amount of calculation will be increased if the network includes too many layers). the output of each layer is recorded as x^(l), l=0, 1, 2, . . . , L, wherein x⁰ is a network input and then the output of an l_(th) layer is: x ^(l) =f _(l)(W ^(l) x ^(l-1) +b ^(l)), l=1,2, . . . ,L  (15) wherein W^(l) and b^(l) respectively indicate the weight and the bias term of the l_(th) layer, f_(l)(⋅) is an activation function of the l_(th) layer. l_(th)=1, 2, . . . , L−1 can be considered as the feature of the network input x⁰, and the output of the l_(th) layer is a network prediction.

e: Using a Deep Learning Network to Train a Mapping Relationship Between the Low-resolution Image Sparse Coefficient and the Difference Image Sparse Coefficient

Features are extracted and predicted layer by layer by using the low-resolution image sparse coefficient α_(l) _(_) _(train) as the input of the deep learning network and using the difference image sparse coefficient α_(hl) _(_) _(train) as a supervision, so as to obtain a network-predicted difference image sparse coefficient: {circumflex over (α)}_(hl) _(_) _(train) =f _(L)( . . . f ₁(W ¹α_(l) _(_) _(train) +b ¹))  (16) wherein W^(l) and b^(l) are respectively the weight and the bias term of the 1st layer.

A root-mean-square error with a cost function of α_(hl) _(_) _(train)−{circumflex over (α)}_(hl) _(_) _(train) is taken

$\begin{matrix} {{MSRE} = \frac{\left. ||{\alpha_{{hl}_{—}{train}} - {\hat{\alpha}}_{{hl}_{—}{train}}}||_{F}^{2} \right.}{mn}} & (17) \end{matrix}$ wherein m and n are respectively the number of dictionary elements and the number of training samples. The network parameters are optimized iteratively so as to minimize a loss function MSRE, thereby completing network training. The network parameters are iteratively adjusted by a gradient descent method in the embodiment; and the training is ended and a trained deep learning network is obtained until the loss function MSRE is less than a given error threshold δ or the number of iterations reaches a given upper limit. In concrete implementation, the value of the error threshold or the number of iterations can be preset by those skilled in the art.

f: Testing the Deep Learning Network

A low-resolution image sparse coefficient test set α_(l) _(_) _(test) is inputted into the trained deep learning network in e, and is outputted to obtain a predicted difference image sparse coefficient {circumflex over (α)}_(hl) _(_) _(test), an error between the {circumflex over (α)}_(hl) _(_) _(test) and a corresponding true difference image sparse coefficient α_(hl) _(_) _(test) is computed, and the deep learning network obtained by training in e is verified as a mapping between the low-resolution image sparse coefficient and the difference image sparse coefficient if the error is less than a given error threshold; otherwise, the network needs to be further trained according to the method in e. In concrete implementation, the error threshold can be preset by those skilled in the art.

g: Reconstructing a High-Resolution Image

The low-resolution image to be subjected to resolution increase is expressed as the matrix form z_(l) in the same manner according to a; z_(l) is linearly expressed with the dictionary D_(l), and a corresponding sparse representation coefficient is recorded as α_(l), to solve β_(l) in the following formula: z _(l) =D _(l)β_(l)  (18)

β_(l) is inputted into the trained deep learning network to obtain a predicted difference image sparse representation coefficient β_(hl). Combined with the difference image dictionary D_(hl), the following formula is used {circumflex over (z)} _(hl) =D _(hl)β_(hl)  (19)

Then a difference portion {circumflex over (z)}_(hl) is reconstructed; and finally a high-resolution image matrix form is reconstructed: {circumflex over (z)} _(h) ={circumflex over (z)} _(hl) +z _(l)  (20)

{circumflex over (z)}_(h) is reduced into an image form to obtain a finally reconstructed high-resolution image z_(h).

In the above steps, steps d to e are a data preparation portion, steps d to e are a network training portion, f is a network test portion and g is an image super-resolution reconstruction portion.

In concrete implementation, the above flow can realize automatic operation by a software technology, and a corresponding system can also be provided by a modular mode. The embodiment of the present disclosure further provides a system for reconstructing a super-resolution image in combination with sparse representation and deep learning, comprising the following modules:

-   -   a first module used for reducing the resolution of an original         high-resolution image to obtain an equal low-resolution image,         respectively expressed as matrix forms y_(h) and y_(l), and         computing a difference portion between two images,         y_(hl)=y_(h)−y_(l);     -   a second module used for respectively conducting dictionary         training on y_(l) and y_(hl) to obtain a low-resolution image         dictionary D_(l), a difference image dictionary D_(hl) and         corresponding sparse representation coefficients α_(l) and         α_(hl);     -   a third module used for dividing the sparse representation         coefficients α_(l) and α_(hl) into training sample coefficients         α_(l) _(_) _(train) and α_(hl) _(_) _(train) and test sample         coefficients α_(l) _(_) _(test) and α_(hl) _(_) _(test);     -   a fourth module used for constructing an L-layer deep learning         network using a root-mean-square error as a cost function;     -   a fifth module used for iteratively optimizing network         parameters so as to minimize the cost function by using the         low-resolution image sparse coefficient α_(l) _(_) _(train) as         the input of the deep learning network, using the corresponding         difference image sparse coefficient α_(hl) _(_) _(train) as a         target output and using {circumflex over (α)}_(hl) _(_) _(train)         as a network-predicted difference image sparse coefficient,         until a trained deep learning network is obtained;     -   a sixth module used for inputting the low-resolution image         sparse coefficient α_(l) _(_) _(test) as the test portion into         the trained deep learning network in the fifth module,         outputting to obtain a predicted difference image sparse         coefficient {circumflex over (α)}_(hl) _(_) _(test), computing         an error between the {circumflex over (α)}_(hl) _(_) _(test) and         a corresponding true difference image sparse coefficient α_(hl)         _(_) _(test), and verifying that the deep learning network         obtained by training in the fifth module is a mapping between         the low-resolution image sparse coefficient and the difference         image sparse coefficient when the error is less than a given         threshold; and     -   a seventh module used for expressing the low-resolution image to         be subjected to resolution increase as the matrix form z_(l),         expressing z_(l) with the dictionary D_(l), recording a         corresponding sparse coefficient as β_(l), inputting β_(l) into         the trained deep learning network to obtain a predicted         difference image sparse coefficient β_(hl), reconstructing a         difference portion {circumflex over (z)}_(hl), with the         dictionary D_(hl); and finally reducing {circumflex over         (z)}={circumflex over (z)}_(hl)+z_(l) into an image form to         reconstruct a corresponding high-resolution image z_(h).

For concrete realization of each module, see corresponding steps which will not be repeated again in the present disclosure. 

The invention claimed is:
 1. A method for reconstructing a super-resolution image, comprising: a) reducing the resolution of an original high-resolution image to obtain an equal low-resolution image, respectively expressed as matrix forms y_(h) and y_(l), and computing a difference portion between two images, y_(hl)=y_(h)−y_(l); b) respectively conducting dictionary training on y_(l) and y_(hl) to obtain a low-resolution image dictionary D_(l), a difference image dictionary D_(hl) and corresponding sparse representation coefficients α_(l) and α_(hl); c) dividing the sparse representation coefficients α_(l) and α_(hl) into training sample coefficients α_(l) _(_) _(train) and α_(hl) _(_) _(train) and test sample coefficients α_(l) _(_) _(test) and α_(hl) _(_) _(test); d) constructing an L-layer deep learning network using a root-mean-square error as a cost function; e) iteratively optimizing network parameters so as to minimize the cost function by using the low-resolution image sparse coefficient α_(l) _(_) _(train) as the input of the deep learning network, using the corresponding difference image sparse coefficient α_(hl) _(_) _(train) as a target output and using {circumflex over (α)}_(hl) _(_) _(train) as a network-predicted difference image sparse coefficient, until a trained deep learning network is obtained; f) inputting the low-resolution image sparse coefficient α_(l) _(_) _(test) as the test portion into the trained deep learning network in e), outputting to obtain a predicted difference image sparse coefficient {circumflex over (α)}_(hl) _(_) _(test), computing an error between the {circumflex over (α)}_(hl) _(_) _(test) and a corresponding true difference image sparse coefficient α_(hl) _(_) _(test), and verifying that the deep learning network obtained by training in e) is a mapping between the low-resolution image sparse coefficient and the difference image sparse coefficient when the error is less than a given threshold; and g) expressing the low-resolution image to be subjected to resolution increase as the matrix form z_(l), expressing z_(l) with the dictionary D_(l), recording a corresponding sparse coefficient as β_(l), inputting β_(l) into the trained deep learning network to obtain a predicted difference image sparse coefficient β_(hl), reconstructing a difference portion {umlaut over (z)}_(hl) with the dictionary D_(hl); and finally reducing {umlaut over (z)}_(h)={umlaut over (z)}_(hl)+z_(l) into an image form to reconstruct a corresponding high-resolution image z_(h).
 2. The method of claim 1, wherein in a), firstly, a high-resolution image in a training sample library is cut into N d×dimage blocks; the resolution of each image block is reduced to obtain N corresponding low-resolution image blocks; then column vectors formed by stretching the high-resolution image blocks compose a matrix y_(h)∈R^(d) ² ^(×N), and column vectors formed by stretching the low-resolution image blocks compose a matrix y_(l)∈R^(d) ² ^(×N); and the difference portion y_(hl)=y_(h)−y_(l) of two matrixes is obtained through computation.
 3. The method of claim 1, wherein in b), dictionary training is respectively conducted on y_(l) and y_(hl) to obtain a corresponding low-resolution image dictionary D_(l), a difference image dictionary D_(hl) and corresponding sparse representation coefficients α_(l) and α_(hl), equivalent to solving optimization problems as follows: $\min\limits_{D_{l},\alpha_{l}}\left. ||\alpha_{l}||{}_{0}\mspace{14mu}{{subject}\mspace{14mu}{to}}\mspace{14mu}||{y_{l} - {D_{l}\alpha_{l}}}\mathop{\text{||}}_{F}^{2}{\leq ɛ} \right.$ $\min\limits_{D_{hl},\alpha_{hl}}\left. ||\alpha_{hl}||{}_{0}\mspace{14mu}{{subject}\mspace{14mu}{to}}\mspace{14mu}||{y_{hl} - {D_{hl}\alpha_{hl}}}\mathop{\text{||}}_{F}^{2}{\leq ɛ} \right.$ wherein ε is a reconstruction error threshold.
 4. The method of claim 1, wherein in d), the constructed deep learning network comprises L layers; the output of each layer is recorded as x^(l), l=0, 1, 2, . . . , L, wherein x⁰ is a network input and then the output of an l_(th) layer is: x ^(l) =f _(l)(W ^(l) x ^(l-1) +b ^(l)), l=1,2, . . . ,L wherein W^(l) and b^(l) respectively indicate the weight and the bias term of the l_(th) layer, f_(l)(⋅) is an activation function of the l_(th) layer, and the output of the l_(th) layer is a network prediction.
 5. The method of claim 1, wherein in e), an implicit relationship between the low-resolution image sparse coefficient α_(l) _(_) _(train) and the difference image sparse coefficient α_(hl) _(_) _(train) is trained by the deep learning network, and by using the low-resolution image sparse coefficient α_(l) _(_) _(train) as the input of the deep learning network and using the difference image sparse coefficient α_(hl) _(_) _(train) as a supervision, the network-predicted difference image sparse coefficient is recorded as {circumflex over (α)}_(hl) _(_) _(train) =f _(L)( . . . f _(l)(W ^(l)α_(l) _(_) _(train) +b ¹))  (4) wherein W^(l) and b^(l) respectively indicate the weight and the bias term of the 1st layer, f_(l)(⋅) is an activation function of the 1st layer, and f_(L) is an activation function of the Lth layer; and a root-mean-square error with a cost function of α_(hl) _(_) _(train)−{circumflex over (α)}_(hl) _(_) _(train) is taken, ${MSRE} = \frac{\left. ||{\alpha_{{hl}_{—}{train}} - {\hat{\alpha}}_{{hl}_{—}{train}}}||_{F}^{2} \right.}{mn}$ wherein m and n are respectively the number of dictionary elements and the number of training samples; and the network parameters are optimized iteratively so as to minimize a loss function MSRE, thereby completing network training. 